Decision making under certainty, uncertainty and risk

DECISION MAKING UNDER CERTAINTY, UNCERTAINTY, AND RISK

Virtually all decisions are made in an environment of at least some uncertainty. However, the degree will vary from relative certainty to great uncertainty. There are certain risks involved in making decisions.

In a situation involving certainty, people are reasonably sure about what will happen when they make a decision. The information is available and is considered to be reliable, and the cause and effect relationships are known.

In a situation of uncertainty, on the other hand, people have only a meager data base, they do not know whether or not the data are reliable, and they are very unsure about whether or not situation may change. Moreover, they cannot evaluate the interactions of the different variables. For examples, a corporation that decides to expand its operation in a strange country may know little about the countryâ€™s culture, laws, economic environment, and politics. The political situation may be so volatile that even the experts cannot predict a possible change in government.

In a risk situation, factual information may exist, but it may be incomplete. To improve decision making, one may estimate the objective probabilities of an outcome by using, for example, mathematical models. On the other hand, subjective probability, based on judgment and experience, may be used. Fortunately, there are a number of tools available that helps make more effective decisions.

Modern approaches to decision making under uncertainty

A number of modern techniques improve the quality of decision making under the normal condition of uncertainty. Among the most important of these are(1) risk analysis, (2) Decision trees

Risk Analysis

All intelligent decision makers dealing with uncertainty like to know the size and nature of the risk they are taking in choosing a course of action. One of the deficiencies in using the traditional approaches of operations research for problem solving is that many of the data used in a model are merely estimates and others are based upon probabilities. The ordinary practice is to have staff specialists come up with â€œbest estimates.â€? However, new techniques have been developed that give a more precise view of risk.

Decision Trees

One of the best ways to analyze a decision is to use so-called decision trees. Decision trees depict, in the form of a â€œtreeâ€? the decision points, chance events, and probabilities involved in various courses that might be undertaken. A common problem occurs in business when a new product is introduced. Managers must decide whether to install expensive permanent equipment to ensure production at the lowest possible cost or to undertake cheaper, temporary tooling that will involve a higher manufacturing cost but lower capital investments and will result in lower losses if the product does not sell as well as estimated, in its simplest form.

The decision tree approach makes it possible to see at least the major alternatives and the fact that subsequent decision may depend upon events in the future. By incorporating the probabilities of various events into the tree, mangers can also comprehend the true probability of a decisionâ€™s leading to the desired results.

Our conclusion is that the above is more theoretical. In practice managers by virtue of their experience, prudence, market forecasts, observing development of competitorâ€™s strategy take appropriate decisions. The project altogether may be called off in case of sudden and unforeseen changes in the market forces and environment even after a short period has elapsed from the start up because at this juncture the financial burden suffered would have been less as compared to going ahead and suffering higher losses or financial expenses at a later stage.

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